A phylogenetic transform enhances analysis of compositional microbiota data

eLife

Volumn 6, Issue , 2017, Pages

  Silverman, Justin D ^a   Washburne, Alex D ^a,b   Mukherjee, Sayan ^a,c,d,e   David, Lawrence A ^a,d  

^a DUKE UNIVERSITY   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c Duke University   (United States)

^d DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e Duke University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTINOBACTERIA; ARTICLE; CLADISTICS; CONTROLLED STUDY; FUSOBACTERIA; GENETIC ANALYSIS; GENETIC VARIABILITY; HUMAN; INFORMATION PROCESSING; MICROBIAL COMMUNITY; MICROBIAL IDENTIFICATION; MICROFLORA; MOLECULAR BIOLOGY; NONHUMAN; PHYLOGENY; PREVOTELLA; STREPTOCOCCUS; BIOLOGY; BIOSTATISTICS; PROCEDURES;

BIOSTATISTICS; COMPUTATIONAL BIOLOGY; HUMANS; MICROBIOTA;

EID: 85014129703     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.21887     Document Type: Article

References (73)

1. Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. 2005. Defining the normal bacterial flora of the oral cavity. Journal of Clinical Microbiology 43:5721–5732. doi: 10.1128/JCM.43.11.5721-5732.2005, PMID: 16272510
2. Aitchison J. 1986. The Statistical Analysis of Compositional Data. London; New York: Chapman and Hall.
3. Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biology 11:R106. doi: 10.1186/gb-2010-11-10-r106, PMID: 20979621
4. Bacon-Shone J. 2011. A Short History of Compositional Data Analysis. Pawlowsky-Glahn V, Buccianti A (eds). Compositional Data Analysis. Hoboken, NJ: John Wiley & Sons, Ltd. p. 1–11.
5. Bear J, Billheimer D. 2016. A logistic normal mixture model for compositional data allowing essential zeros. Austrian Journal of Statistics 45:3–23. doi: 10.17713/ajs.v45i4.117
6. Billheimer D, Guttorp P, Fagan WF. 2001. Statistical interpretation of species composition. Journal of the American Statistical Association 96:1205–1214. doi: 10.1198/016214501753381850
7. Blaser MJ, Falkow S. 2009. What are the consequences of the disappearing human Microbiota? Nature Reviews Microbiology 7:887–894. doi: 10.1038/nrmicro2245, PMID: 19898491
8. Britanova OV, Putintseva EV, Shugay M, Merzlyak EM, Turchaninova MA, Staroverov DB, Bolotin DA, Lukyanov S, Bogdanova EA, Mamedov IZ, Lebedev YB, Chudakov DM. 2014. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. The Journal of Immunology 192:2689–2698. doi: 10.4049/jimmunol.1302064, PMID: 24510963
9. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108 Suppl 1: 4516–4522. doi: 10.1073/pnas.1000080107, PMID: 20534432
10. Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H. 2012. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics 28:2106–2113. doi: 10.1093/bioinformatics/bts342, PMID: 22711789
11. Chen J, Li H. 2013. Variable selection for sparse Dirichlet-multinomial regression with an application to microbiome data analysis. The Annals of Applied Statistics 7:418–442. doi: 10.1214/12-AOAS592, PMID: 24312162
12. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI, Knight R. 2009. Bacterial community variation in human body habitats across space and time. Science 326:1694–1697. doi: 10.1126/science.1177486, PMID: 19892944
13. Culley AI, Lang AS, Suttle CA. 2006. Metagenomic analysis of coastal RNA virus communities. Science 312:1795–1798. doi: 10.1126/science.1127404, PMID: 16794078
14. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, Collini S, Pieraccini G, Lionetti P. 2010. Impact of diet in shaping gut Microbiota revealed by a comparative study in children from Europe and rural Africa. PNAS 107:14691–14696. doi: 10.1073/pnas.1005963107, PMID: 20679230
15. Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C. 2003. Isometric logratio transformations for compositional data analysis. Mathematical Geology 35:279–300. doi: 10.1023/A:1023818214614
16. Egozcue JJ, Pawlowsky-Glahn V. 2005. Groups of parts and their balances in compositional data analysis. Mathematical Geology 37:795–828. doi: 10.1007/s11004-005-7381-9
17. Egozcue JJ, Pawlowsky-Glahn V. 2016. Changing the reference measure in the simplex and its weighting effects. Austrian Journal of Statistics 45:25–44. doi: 10.17713/ajs.v45i4.126
18. Fang H, Huang C, Zhao H, Deng M. 2015. CCLasso: correlation inference for compositional data through lasso. Bioinformatics 31:3172–3180. doi: 10.1093/bioinformatics/btv349, PMID: 26048598
19. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower C. 2012. Microbial co-occurrence relationships in the human microbiome. PLoS Computational Biology 8:e1002606. doi: 10.1371/journal.pcbi.1002606, PMID: 22807668
20. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. 2014. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:15. doi: 10.1186/2049-2618-2-15, PMID: 24910773
21. Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS. 2014. A taxonomic signature of obesity in the microbiome? getting to the guts of the matter. PLoS One 9:e84689. doi: 10.1371/journal.pone.0084689, PMID: 24416266
22. Friedman J, Alm EJ. 2012. Inferring correlation networks from genomic survey data. PLoS Computational Biology 8:e1002687. doi: 10.1371/journal.pcbi.1002687, PMID: 23028285
23. Fukuyama J, McMurdie PJ, Dethlefsen L, Relman DA, Holmes S. 2012. Comparisons of distance methods for combining covariates and abundances in microbiome studies. Pacific Symposium on Biocomputing. NIH Public Access. p. 213
24. Gloor GB, Macklaim JM, Vu M, Fernandes AD. 2016a. Compositional uncertainty should not be ignored in high-throughput sequencing data analysis. Austrian Journal of Statistics 45:73. doi: 10.17713/ajs.v45i4.122
25. Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. 2016b. It’s all relative: analyzing microbiome data as compositions. Annals of Epidemiology 26:322–329. doi: 10.1016/j.annepidem.2016.03.003, PMID: 27143475
26. Good IJ. 1956. On the estimation of small frequencies in Contingency-Tables. Journal of the Royal Statistical Society Series B-Statistical Methodology 18:113–124.
27. Grice EA, Segre JA. 2011. The skin microbiome. Nature Reviews Microbiology 9:244–253. doi: 10.1038/nrmicro2537, PMID: 21407241
28. Human Microbiome Project Consortium. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:207–214. doi: 10.1038/nature11234, PMID: 22699609
29. Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. 2008. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320:1081–1085. doi: 10.1126/science.1157890, PMID: 18497299
30. Jackson DA. 1997. Compositional data in community ecology: the paradigm or peril of proportions? Ecology 78: 929–940. doi: 10.1890/0012-9658(1997)078[0929:CDICET]2.0.CO;2
31. Janda JM, Abbott SL. 2007. 16s rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. Journal of Clinical Microbiology 45:2761–2764. doi: 10.1128/JCM.01228-07, PMID: 17626177
32. Knights D, Costello EK, Knight R. 2011. Supervised classification of human Microbiota. FEMS Microbiology Reviews 35:343–359. doi: 10.1111/j.1574-6976.2010.00251.x, PMID: 21039646
33. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. 2015. Sparse and compositionally robust inference of microbial ecological networks. PLOS Computational Biology 11:e1004226. doi: 10.1371/journal.pcbi.1004226, PMID: 25950956
34. La Rosa PS, Brooks JP, Deych E, Boone EL, Edwards DJ, Wang Q, Sodergren E, Weinstock G, Shannon WD. 2012. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS ONE 7: e52078. doi: 10.1371/journal.pone.0052078, PMID: 23284876
35. Lee SC, Tang MS, Lim YA, Choy SH, Kurtz ZD, Cox LM, Gundra UM, Cho I, Bonneau R, Blaser MJ, Chua KH, Loke P. 2014. Helminth colonization is associated with increased diversity of the gut Microbiota. PLoS Neglected Tropical Diseases 8:e2880. doi: 10.1371/journal.pntd.0002880, PMID: 24851867
36. Levy R, Borenstein E. 2013. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. PNAS 110:12804–12809. doi: 10.1073/pnas.1300926110, PMID: 23858463
37. Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R, Gordon JI. 2008. Evolution of mammals and their gut microbes. Science 320:1647–1651. doi: 10. 1126/science.1155725, PMID: 18497261
38. Lê Cao KA, Costello ME, Lakis VA, Bartolo F, Chua XY, Brazeilles R, Rondeau P. 2016. MixMC: a multivariate statistical framework to gain insight into microbial communities. PLoS One 11:e0160169. doi: 10.1371/journal.pone.0160169, PMID: 27513472
39. Li H. 2015. Microbiome, metagenomics, and high-dimensional compositional data analysis. Annual Review of Statistics and Its Application 2:73–94. doi: 10.1146/annurev-statistics-010814-020351
40. Lin W, Shi P, Feng R, Li H. 2014. Variable selection in regression with compositional covariates. Biometrika 101: 785–797. doi: 10.1093/biomet/asu031
41. Lovell D, Müller W, Taylor J, Zwart A, Helliwell C. 2011. Proportions, percentages, ppm: do the molecular biosciences treat compositional data right. Pawlowsky-Glahn V, Buccianti A (eds). Compositional Data Analysis: Theory and Applications. Hoboken, NJ: John Wiley & Sons, Ltd. p. 193–207.
42. Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bähler J. 2015. Proportionality: a valid alternative to correlation for relative data. PLOS Computational Biology 11:e1004075. doi: 10.1371/journal.pcbi.1004075, PMID: 25775355
43. Lozupone C, Knight R. 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71:8228–8235. doi: 10.1128/AEM.71.12.8228-8235.2005, PMID: 16332807
44. Mager DL, Ximenez-Fyvie LA, Haffajee AD, Socransky SS. 2003. Distribution of selected bacterial species on intraoral surfaces. Journal of Clinical Periodontology 30:644–654. doi: 10.1034/j.1600-051X.2003.00376.x, PMID: 12834503
45. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health & Disease 26: 27663. doi: 10.3402/mehd.v26.27663, PMID: 26028277
46. Martın-Fernandez JA, Palarea-Albaladejo J, Olea RA. 2011. Dealing with zeros. Pawlowsky-Glahn V, Buccianti A (eds). Compositional Data Analysis: Theory and Applications. Hoboken, NJ: John Wiley & Sons, Ltd. p. 43–58.
47. Martin-Fernandez J-A, Hron K, Templ M, Filzmoser P, Palarea-Albaladejo J. 2015. Bayesian-multiplicative treatment of count zeros in compositional data sets. Statistical Modelling 15:134–158. doi: 10.1177/1471082X14535524
48. Martiny JB, Jones SE, Lennon JT, Martiny AC. 2015. Microbiomes in light of traits: a phylogenetic perspective. Science 350:aac9323. doi: 10.1126/science.aac9323, PMID: 26542581
49. Matsen FA. 2015. Phylogenetics and the human microbiome. Systematic Biology 64:e26–e41. doi: 10.1093/sysbio/syu053, PMID: 25102857
50. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of Bacteria and archaea. The ISME Journal 6:610–618. doi: 10.1038/ismej.2011.139, PMID: 22134646
51. McMurdie PJ, Holmes S. 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217. doi: 10.1371/journal.pone.0061217, PMID: 23630581
52. McMurdie PJ, Holmes S. 2014. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Computational Biology 10:e1003531. doi: 10.1371/journal.pcbi.1003531, PMID: 24699258
53. Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, Pusey AE, Peeters M, Hahn BH, Ochman H. 2016. Cospeciation of gut microbiota with hominids. Science 353:380–382. doi: 10.1126/science.aaf3951, PMID: 27463672
54. Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, Navas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, Knight R. 2017. Balance trees reveal microbial niche differentiation. mSystems 2:e00162-16. doi: 10.1128/mSystems.00162-16, PMID: 28144630
55. Paradis E, Claude J, Strimmer K. 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290. doi: 10.1093/bioinformatics/btg412, PMID: 14734327
56. Paulson JN, Stine OC, Bravo HC, Pop M. 2013. Differential abundance analysis for microbial marker-gene surveys. Nature Methods 10:1200–1202. doi: 10.1038/nmeth.2658, PMID: 24076764
57. Pawlowsky-Glahn V, Buccianti A. 2011. Compositional Data Analysis: Theory and Applications. Hoboken, NJ: John Wiley & Sons, Ltd.
58. Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. 2015. Modeling and Analysis of Compositional Data. Hoboken, NJ: John Wiley & Sons, Ltd.
59. Pawlowsky-Glahn V, Egozcue JJ. 2011. Exploring compositional data with the CoDa-Dendogram. Austrian Journal of Statistics 40:103–113.
60. Purdom E. 2011. Analysis of a data matrix and a graph: metagenomic data and the phylogenetic tree. The Annals of Applied Statistics 5:2326–2358. doi: 10.1214/10-AOAS402
61. Rakoff-Nahoum S, Foster KR, Comstock LE. 2016. The evolution of cooperation within the gut microbiota. Nature 533:255–259. doi: 10.1038/nature17626, PMID: 27111508
62. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, Ha G, Aparicio S, Bouchard-Côté A, Shah SP. 2014. PyClone: statistical inference of clonal population structure in Cancer. Nature Methods 11:396–398. doi: 10.1038/nmeth. 2883,PMID: 24633410
63. Schliep KP. 2011. Phangorn: phylogenetic analysis in R. Bioinformatics 27:592–593. doi: 10.1093/bioinformatics/btq706, PMID: 21169378
64. Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ. 2011. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480:241–244. doi: 10.1038/nature10571, PMID: 22037308
65. Tsilimigras MC, Fodor AA. 2016. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Annals of Epidemiology 26:330–335. doi: 10.1016/j.annepidem.2016.03.002, PMID: 27255738
66. van den Boogaart KG, Tolosana-Delgado R. 2013. Analyzing Compositional Data with R. New York: Springer.
67. Větrovský T, Baldrian P. 2013. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 8:e57923. doi: 10.1371/journal.pone.0057923, PMID: 23460914
68. Waldor MK, Tyson G, Borenstein E, Ochman H, Moeller A, Finlay BB, Kong HH, Gordon JI, Nelson KE, Dabbagh K, Smith H. 2015. Where next for microbiome research? PLOS Biology 13:e1002050. doi: 10.1371/journal.pbio.1002050, PMID: 25602283
69. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li H, Bushman FD, Lewis JD, Gd W. 2011. Linking long-term dietary patterns with gut microbial enterotypes. Science 334:105–108. doi: 10.1126/science.1208344, PMID: 21885731
70. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP, Heath AC, Warner B, Reeder J, Kuczynski J, Caporaso JG, Lozupone CA, Lauber C, Clemente JC, Knights D, Knight R, et al. 2012. Human gut microbiome viewed across age and geography. Nature 486:222–227. doi: 10.1038/nature11053, PMID: 22699611
71. Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. 2017. Ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 8:28–36. doi: 10.1111/2041-210X.12628
72. Yuan K, Sakoparnig T, Markowetz F, Beerenwinkel N. 2015. BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biology 16:36. doi: 10.1186/s13059-015-0592-6, PMID: 257 86108
73. Zaneveld JR, Lozupone C, Gordon JI, Knight R. 2010. Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives. Nucleic Acids Research 38:3869–3879. doi: 10.1093/nar/gkq066, PMID: 20197316